Daniel Kifer

Professor of Computer Science & Engineering

Daniel Kifer

Publication Tags

Learning Neural Networks Convolutional Neural Networks Statistics Crime Semantics Soil Moisture Simulation Neurons Moisture Pixels Prediction Carbon Atom Prolongation Big Data Coding Community Convolution Science Regression Flow Laboratory Physics Offense Opinion

Most Recent Papers

The neural coding framework for learning generative models

Alexander Ororbia, Daniel Kifer, 2022, Nature Communications

The Data Synergy Effects of Time-Series Deep Learning Models in Hydrology

Kuai Fang, Daniel Kifer, Kathryn Lawson, Dapeng Feng, Chaopeng Shen, 2022, Water Resources Research

Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms

Zeyu Ding, Yuxin Wang, Yingtai Xiao, Guanhong Wang, Danfeng Zhang, Daniel Kifer, 2022, VLDB Journal


Yuxin Wang, Zeyu DIng, Yingtai Xiao, Daniel Kifer, Danfeng Zhang, 2021, on p. 393-411

Physics-informed deep learning for prediction of CO<sub>2</sub> storage site response

Parisa Shokouhi, Vikas Kumar, Sumedha Prathipati, Seyyed A. Hosseini, Clyde Lee Giles, Daniel Kifer, 2021, Journal of Contaminant Hydrology

Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data

Parisa Shokouhi, Vrushali Girkar, Jacques Rivière, Srisharan Shreedharan, Chris Marone, C. Lee Giles, Daniel Kifer, 2021, Geophysical Research Letters


Shivansh Rao, Vikas Kumar, Daniel Kifer, C. Lee Giles, Ankur Mali, 2021, on p. 3701-3710

Network Spillovers and Neighborhood Crime

Corina Graif, Brittany N. Freelin, Yu Hsuan Kuo, Hongjian Wang, Zhenhui Li, Daniel Kifer, 2021, Justice Quarterly on p. 344-374

Optimizing fitness-for-use of differentially private linear Queries

Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer, 2021, Proceedings of the VLDB Endowment on p. 1730-1742

An Uncertainty Principle is a Price of Privacy-Preserving Microdata

John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev, 2021,

Most-Cited Papers


Daniel Kifer, Ashwin Machanavajjhala, 2014, ACM Transactions on Database Systems

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Kuai Fang, Chaopeng Shen, Daniel Kifer, Xiao Yang, 2017, Geophysical Research Letters on p. 11,030-11,039

HESS opinions: Incubating deep-learning-powered hydrologic science advances as a community

Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi John Chang, Sangram Ganguly, Kuo Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, Wen Ping Tsai, 2018, Hydrology and Earth System Sciences on p. 5639-5656

Learning to extract semantic structure from documents using multimodal fully convolutional neural networks

Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles, 2017, on p. 4342-4351

Learning to read irregular text with attention mechanisms

Xiao Yang, Dafang He, Zihan Zhou, Daniel Kifer, C. Lee Giles, 2017, on p. 3280-3286

Crime rate inference with big data

Hongjian Wang, Daniel Kifer, Corina Graif, Zhenhui Li, 2016, on p. 635-644

A rigorous and customizable framework for privacy

Daniel Kifer, Ashwin Machanavajjhala, 2012, on p. 77-88

Multi-scale FCN with cascaded instance aware segmentation for arbitrary oriented word spotting in the wild

Dafang He, Xiao Yang, Chen Liang, Zihan Zhou, Alex G. Ororbia, Daniel Kifer, C. Lee Giles, 2017, on p. 474-483

Multi-Scale Multi-Task FCN for Semantic Page Segmentation and Table Detection

Dafang He, Scott Cohen, Brian Price, Daniel Kifer, C. Lee Giles, 2017, on p. 254-261

Private convex empirical risk minimization and high-dimensional regression

Daniel Kifer, Adam Smith, Abhradeep Thakurta, 2012, Journal of Machine Learning Research on p. 25.1-25.40